Del storyboard al plano

We combined all the pay and benefits detailed above into annual av- erages for each grade and applied one-year inflation of 3.2 percent to translate the 1996 rates into 1997 dollars. Table B.2 displays these annual compensation rates for the AC and RC by grade and grouped by SROTC position. We calculated the annual compensation for RC personnel using a standard 48-drill inactive-duty training year and 18.5-day AT. A typical schedule for a reservist in a TPU involves drilling one weekend per month during the year (48 drills) plus two weeks during the summer (14-day AT). Because of the need to staff SROTC summer camps and accompany the battalions on weekend exercises during the school year, we estimate that reservists would average 48 drills plus 18.5 days AT. The increased costs for the extra

Military Personnel Costs 57

Table B.2

Annual Compensation Rates (1997$)

Position Active Reserve

and Grade Compensation Distribution Compensation Distribution Instructor O-6 128,068 0.0% 16,938 7.0% O-5 107,597 0.0% 14,029 24.8% O-4 88,262 28.8% 11,662 29.1% O-3 71,006 71.2% 9,797 39.1% Average 75,981 11,888 Trainer E-8 66,510 10.8% 8,225 0.0% E-7 57,915 89.2% 7,017 100.0% Average 58,845 7,017 Admin/Log E-6 49,870 50.9% 5,557 19.3% E-5 41,993 49.1% 4,885 27.2% E-4 34,298 0.0% 3,998 53.5% Average 45,999 4,540

4.5 days AT would represent incremental cash costs for using RC per- sonnel in SROTC.

We assumed that reservist compensation for each grade would be the same for members of the IRR or Selected Reserves. As noted in the text, IRR members could function in two different statuses, but at equivalent compensation. They could join the Selected Reserves and participate in SROTC through a TPU, earning pay and benefits as de- scribed above. Alternatively, they could remain in the IRR but be ac- tivated with a specified rate of pay for each period worked. (Although more complicated, this method might be employed be- cause these IRR members would not count against the endstrength ceiling for the Selected Reserves as long as they do not serve on con- tinuous duty for more than 180 days.)

To compute average costs for each AC and RC position, we used grade distributions. For AC positions to be replaced, we used an es- timate of the grades that would likely be subject to replacement for each current position. For RC replacements, we used estimates of

58 Staffing Army ROTC at Colleges and Universities

the populations that would be available to fill each position. For in- structors, we used the distribution of O-3 through O-6 TPU members whose civilian occupations are in secondary or postsecondary teaching. For trainers, we used the average E-7 pay rate. For the admin/log support positions, we used the population distribution of E-4 through E-6 in the OCAR-designated admin/log MOSs.1 _Popu-

lations were derived from SIDPERS using the appropriate criteria. To compute the estimated costs of the TPU/IRR SROTC battalion staffing, we combined these average rates with the staffing plan in Chapter Two of the report.

______________

1_{This admin/log support position is used to fill part of the replacements for APMS and}

59

Appendix C

GEOGRAPHIC AVAILABILITY MODEL

With the current AC and AGR staffing for SROTC battalions, the Army orders soldiers to report to the college locations as required to staff each battalion. Alternative staffing plans would not allow the Army to directly order personnel to fill vacancies. Instead, positions would be filled by reservists or contracted civilians.

Since reservists are typically employed (aside from their part-time re- serve duty), they may well be unlikely to move in order to accept a part-time SROTC position. We developed a geographic availability model to estimate the number of reservists who would be able to staff SROTC positions, based on the population of reservists within 50 miles of each SROTC program.

The contractor employing civilians, including former military, might be able to induce potential staff to locate near available positions. But the preferences of individuals—and the limited permitted job tenure for former military—would reduce the chance that civilians would locate much differently from current patterns. We therefore estimated the availability of civilians within 50 miles of each SROTC program using a geographic model based on the actual location of civilians with the required qualifications as reported in the 1990 U.S. Census.

The data for civilians is derived from the 5 percent Public Use Micro- data Sample (PUMS) of the 1990 U.S. Census, weighted to represent the entire U.S. population. We selected the observations with the appropriate military experience, education, and occupation as de- scribed in Chapter Two of the main text. Separate pools of observa- tions were constructed for instructor, trainer, and admin/log eligible

60 Staffing Army ROTC at Colleges and Universities

populations. As described, we dropped observations whose reported wages exceeded the thresholds for each pool.

We then calculated a scaling percentage for instructors, trainers, and admin/log by combining all of the factors described in the main text. These factors included military force size reductions and estimated awareness of and interest in the jobs.

Using the present list of SROTC battalions, we applied the proposed replacement staffing plan to derive the number of instructors, train- ers, and admin/log positions desired at each school. We termed this desired number of positions the demand at each school.

The geographic model then matched the estimated pool of available personnel to the desired replacements. For each school, we used the school zip code to derive latitude and longitude from a database de- signed for that purpose to calculate the number of potential re- placements within 50 miles of the school.

For the observations from the PUMS, individual-level zip codes are not provided (to preserve confidentiality). Instead, observations are coded in Public Use Microdata Areas (PUMAs), which encompass defined areas. Urban PUMAs are generally small, though rural PUMAs can be large. For all observations, we allocated the persons in each PUMA to the individual zip codes in the PUMA according to the general population in each zip code.

A key technical concern was to allocate as many people as possible to the positions. Each school could draw from an area within 50 miles. Since a number of schools had overlapping areas, we developed a model to handle the overlapping areas without assigning more of the pool to any school than that school’s demand. The model made several passes through the list of schools to optimally allocate the pool of observations to school positions.

We used the same basic model to estimate the geographic availability of TPU and IRR reserve personnel. Since Army databases listed home zip codes for each reservist, no preliminary allocation was re- quired as with the PUMAs. Otherwise, the model proceeded in the same way as for the civilians.

Geographic Availability Model 61

Early model runs pointed up that the key constraints on reserve re- placements were the reservists qualified as part-time instructors and trainers, rather than the part-time admin/log support personnel. Therefore, the final estimates of the reservists’ ability to replace full- time military are based only on the instructors (four TPU/IRR per position) or trainers (six TPU/IRR per position). In many schools, the model computed that there would not be sufficient personnel to meet the full demand. We computed the total number of full-time positions that could be replaced. We consider one full-time instruc- tor position replaced if the school had more than 0.5 of the required TPU/IRR instructors but less than 1.5. If the school had more than 1.5 but less than 2.5, we considered two positions replaced, and so forth. The same applied to trainer positions. After computing the number of full-time positions replaced at each school, we summed over all schools to compute the total.

63

Appendix D

STATISTICAL POWER CALCULATIONS

The main text indicates the basic methods used to estimate the sta- tistical power of the measures for commissions, workload, and Ad- vanced Camp scores.